Foreground detection provides both a means of efficient allocation of computational resources and a method for reducing false-positives when determining which parts of a video sequence are “important” for some desired purpose. Many traditional methods of foreground detection consider the changes in pixel values between frames. Some methods of filtration—such as a median filter—are often used, and are described, for example, in P-M. Jodoin, S. Piérard, Y. Wang, and M. Van Droogenbroeck, “Overview and Benchmarking of Motion Detection Methods,” Background Modeling and Foreground Detection for Video Surveillance, Chapter 1, which is hereby incorporated by reference in its entirety for all purposes. A more recently developed approach is the use of a fractal measure applied to a portion of a video frame, with a fractal dimensionality of the joint histogram suggesting a contextual change, distinct from a local lighting change; here, the dimensionality is measured using a box-counting method, as described by Farmer in M. E. Farmer, “A Chaos Theoretic Analysis of Motion and Illumination in Video Sequences”, Journal of Multimedia, Vol. 2, No. 2, 2007, pp. 53-64; and M. E. Farmer, “Robust Pre-Attentive Attention Direction Using Chaos Theory for Video Surveillance”, Applied Mathematics, 4, 2013, pp. 43-55, each of which is hereby incorporated by reference in its entirety for all purposes. Searching for explicitly self-similar structures in image physical space has also been used with success to find important parts of an image, as described in H. Li, K. J. R. Lui, and S-C. B. Lo, “Fractal Modeling and Segmentation in the Enhancement of Microcalcifications in Digital Mammograms”, Report by Institute for Systems Research, University of Maryland, College Park, Md., 20742, 1997, which is hereby incorporated by reference in its entirety for all purposes.